Introduction

• Air pollution can be detrimental to both our health and the climate
  • Outdoor and indoor air pollution cause chronic pain, respiratory diseases, shortened lifespan
  • Air pollution kills about 7 million people worldwide every year
  • Hopefully this information can showcase the importance of air pollution and that we should be more mindful about our planet
• Overview
  • We will see how different air pollution types affect the population
  • compare past and present population numbers
  • determine which air pollutant type has the highest associated death rate

Packages Required

Data Details

• Deaths from air pollution:
  • Kaggle: [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017]
  • Author: Akshat Giri
  • Last update: 2 years ago

Import the deaths-due-to-air-pollution data

deaths_df <- data.frame(read.csv("death-rates-from-air-pollution.csv"))

We are going to rename a few of the columns and glimpse the data

colnames(deaths_df) <- c("country", "acronym", "year", "total_deaths", "indoor_deaths", "outdoor_deaths", "ozone_deaths")

glimpse(deaths_df)

## Rows: 6,468
## Columns: 7
## $ country        <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist~
## $ acronym        <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG",~
## $ year           <int> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1~
## $ total_deaths   <dbl> 299.4773, 291.2780, 278.9631, 278.7908, 287.1629, 288.0~
## $ indoor_deaths  <dbl> 250.3629, 242.5751, 232.0439, 231.6481, 238.8372, 239.9~
## $ outdoor_deaths <dbl> 46.44659, 46.03384, 44.24377, 44.44015, 45.59433, 45.36~
## $ ozone_deaths   <dbl> 5.616442, 5.603960, 5.611822, 5.655266, 5.718922, 5.739~

Variables that interest us here include: country, total_deaths, indoor_deaths, outdoor_deaths, ozone_deaths

Now, we are going to import the world-popultaion data and glimpse

world_pop <- read.csv("population_total_long.csv")
glimpse(world_pop)

## Rows: 12,595
## Columns: 3
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "~
## $ Year         <int> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 196~
## $ Count        <int> 54211, 8996973, 5454933, 1608800, 13411, 92418, 20481779,~

To get a general idea of ‘deaths-dataframe’ we made, let’s make a plots to see what’s happening. This is a plot of indoor x outdoor deaths around the world by country.

d <- ggplot(deaths_df, aes(x = indoor_deaths, y = outdoor_deaths, text = paste0(country, ", ", year) )) + geom_point()
ggplotly(d)

This is a mess, and so we chose two countries from each continent (a high-population and a low-population country) to graph.

Exploratory Data Analysis

First, we split the data into high and low population based on country

+Low population = high population * .10

#selecting high-population countries from the world population data frame 
high_pop_countries <- world_pop %>% 
  filter(Year > 1996 & Country.Name %in% c('United States', 'Brazil', 'Nigeria', 'Germany', 'Pakistan', 'Australia')) %>% 
  group_by(Year)

high_pop_countries

## # A tibble: 126 x 3
## # Groups:   Year [21]
##    Country.Name   Year     Count
##    <chr>         <int>     <int>
##  1 Australia      1997  18517000
##  2 Brazil         1997 167209040
##  3 Germany        1997  82034771
##  4 Nigeria        1997 113457663
##  5 Pakistan       1997 131057431
##  6 United States  1997 272657000
##  7 Australia      1998  18711000
##  8 Brazil         1998 169785250
##  9 Germany        1998  82047195
## 10 Nigeria        1998 116319759
## # ... with 116 more rows

#selecting low-population countries from the world population data frame 
low_pop_countries <- world_pop %>% 
  filter(Year > 1996 & Country.Name %in% c('Canada', 'Chile', 'Malawi', 'Serbia', 'Sri Lanka', 'New Zealand'))%>% 
  group_by(Year)

low_pop_countries

## # A tibble: 126 x 3
## # Groups:   Year [21]
##    Country.Name  Year    Count
##    <chr>        <int>    <int>
##  1 Canada        1997 29905948
##  2 Chile         1997 14786220
##  3 Sri Lanka     1997 18470900
##  4 Malawi        1997 10264906
##  5 New Zealand   1997  3781300
##  6 Serbia        1997  7596501
##  7 Canada        1998 30155173
##  8 Chile         1998 14977733
##  9 Sri Lanka     1998 18564599
## 10 Malawi        1998 10552338
## # ... with 116 more rows

#Mean total deaths from 1990-2017 of high-population countries
deaths_highpop_countries <- deaths_df %>% 
  filter(country %in% c('United States', 'Brazil', 'Nigeria', 'Germany', 'Pakistan', 'Australia')) %>% 
  group_by(country) %>% 
  select(total_deaths) %>% 
  summarize(average_death_high = mean(total_deaths))

## Adding missing grouping variables: `country`

#deaths_highpop_countries


#Mean total deaths from 1990-2017 of high-population countries
deaths_lowpop_countries<- deaths_df %>% 
  filter(country %in% c('Canada', 'Chile', 'Malawi', 'Serbia', 'Sri Lanka', 'New Zealand')) %>% 
  group_by(country) %>% 
  select(total_deaths) %>% 
  summarize(average_death_low = mean(total_deaths))

## Adding missing grouping variables: `country`

#death_lowpop_countries
kable(list(deaths_highpop_countries, deaths_lowpop_countries))

kable(deaths_highpop_countries) %>% 
  kable_styling(full_width = FALSE, position = 'float_left')

kable(deaths_lowpop_countries) %>% 
  kable_styling(full_width = FALSE, position = 'left')

ggplot(deaths_highpop_countries)+
  geom_col(mapping = aes(x=country, y=average_death_high))+
             xlab("Country")+
             ylab("Average deaths (per 100,000)")+
             ggtitle("Average total deaths in high-population countries")+
  coord_flip()

ggplot(deaths_lowpop_countries)+
  geom_col(mapping = aes(x=country, y=average_death_low))+
             xlab("Country")+
             ylab("Average deaths (per 100,000)")+
             ggtitle("Average total deaths in low-population countries")+
  coord_flip()

This shows us the deaths due to pollution, but what about the average population of those countries at that time?

hp_countries_population <- world_pop %>% 
  filter(Country.Name %in% c('United States', 'Brazil', 'Nigeria', 'Germany', 'Pakistan', 'Australia')) %>% 
  group_by(Country.Name) %>% 
  select(Count) %>% 
  summarize(average_population = mean(Count))

## Adding missing grouping variables: `Country.Name`

#hp_countries_population

lp_countries_population <- world_pop %>% 
  filter(Country.Name %in% c('Canada', 'Chile', 'Malawi', 'Serbia', 'Sri Lanka', 'New Zealand')) %>% 
  group_by(Country.Name) %>% 
  select(Count) %>% 
  summarize(average_population = mean(Count))

## Adding missing grouping variables: `Country.Name`

#lp_countries_population

kable(list(hp_countries_population, lp_countries_population))

kable(hp_countries_population) %>% 
  kable_styling(full_width = FALSE, position = 'float_left')

kable(lp_countries_population) %>% 
  kable_styling(full_width = FALSE, position = 'left')

ggplot(hp_countries_population)+
  geom_col(mapping = aes(x=Country.Name, y=average_population))+
             xlab("Country")+
             ylab("Average Population")+
             ggtitle("Average high-population countries")+
  coord_flip()

ggplot(lp_countries_population)+
  geom_col(mapping = aes(x=Country.Name, y=average_population))+
             xlab("Country")+
             ylab("Average Population")+
             ggtitle("Average low-population countries")+
  coord_flip()

#Join the data sets so we can overlay the two graph or do a stacked barchart?

• full_join(x, y, by = ):return all rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing.
• full join deaths_df and world_pop2

Summary